Introduction to R for Data Analysis

class: center, middle, inverse, title-slide

# Introduction to R for Data Analysis
### <em>Yadong Liu</em>
### November 2019 (updated: June 2021)

---

background-image: url(data:image/png;base64,#https://d33wubrfki0l68.cloudfront.net/86cc45e87bb755a3bcecce462a6524e68d13a466/67469/images/r4ds/data_science_pipeline.png)
background-size: 80%

# Big Picture

---
background-image: url(data:image/png;base64,#https://www.r-project.org/Rlogo.png)
background-size: 100px
background-position: 30% 50%

class: center, middle

# you ready?

---
class: inverse, center, middle

# 1. Getting Started

---
background-image: url(data:image/png;base64,#https://revolution-computing.typepad.com/.a/6a010534b1db25970b01b8d2594d25970c-800wi)
background-position: 95% 50%
background-size: 350px

## About [R](https://www.r-project.org)

A language and environment for statistical computing and graphics

Created by statisticians for statisticians

**Core/Base + Addon**

- [CRAN](https://cloud.r-project.org) (>15000) `install.packages()`
- [Bioconductor](https://www.bioconductor.org) `remotes::install_bioc()`
- [Github](https://github.com) `remotes::install_github()`

## About [RStudio](https://rstudio.com)

IDE + Packages + Community + Education

---
background-image: url(data:image/png;base64,#https://avatars1.githubusercontent.com/u/22032646?s=200&v=4)
background-size: 100px
background-position: 90% 6%

## About [Tidyverse](https://tidyverse.org)

A collection of packages designed for data science
  - ggplot2, readr, tibble, tidyr, dplyr, stringr, lubridate, purrr, ...

```
install.packages('tidyverse')
library(tidyverse)
```

## About [Hadley Wickham](http://hadley.nz)

Chief Scientist at RStudio

Winner of 2019 [COPSS](https://community.amstat.org/copss/home) Presidents' Award

>For influential work in statistical computing, visualization, graphics, and data analysis; for developing and implementing an impressively comprehensive computational infrastructure for data analysis through R software; for making statistical thinking and computing accessible to large audience; and for enhancing an appreciation for the important role of statistics among data scientists.

---
background-image: url(data:image/png;base64,#https://china-r.org/img/China-R-Logo-trans.png)
background-position: 50% 60%

## About [Yihui Xie|谢益辉](https://yihui.name)

Founder of [Capital of Statistics|统计之都](https://cosx.org)

Initiator of [China-R Conference|中国R语言会议](https://china-r.org)

---
## Data type

logical, integer, double, character, factor, Date, Date-time

**vector & list**

.pull-left[
```r
lgl_var <- c(TRUE, FALSE)
int_var <- c(1L, 6L, 10L)
dbl_var <- c(1, 2.5, 4.5)
chr_var <- c("these are", "some strings")
```
![](data:image/png;base64,#https://d33wubrfki0l68.cloudfront.net/8a3d360c80da1186b1373a0ff0ddf7803b96e20d/254c6/diagrams/vectors/atomic.png)
]

.pull-right[
```r
l1 <- list(
  1:3, 
  "a", 
  c(TRUE, FALSE, TRUE), 
  c(2.3, 5.9)
  )
```
![](data:image/png;base64,#https://d33wubrfki0l68.cloudfront.net/9628eed602df6fd55d9bced4fba0a5a85d93db8a/36c16/diagrams/vectors/list.png)
]

---
** matrix & data frame**

```r
df1 <- data.frame(x = 1:3 * 10)
df1$y <- matrix(1:9, nrow = 3)
df1$z <- data.frame(a = 3:1, b = letters[1:3], stringsAsFactors = FALSE)
str(df1)
```

```
## 'data.frame':	3 obs. of  3 variables:
##  $ x: num  10 20 30
##  $ y: int [1:3, 1:3] 1 2 3 4 5 6 7 8 9
##  $ z:'data.frame':	3 obs. of  2 variables:
##   ..$ a: int  3 2 1
##   ..$ b: chr  "a" "b" "c"
```

---
background-image: url(data:image/png;base64,#https://bookdown.org/wangminjie/R4DS/images/data_struction1.png)
background-size: contain

---
**factor**

```r
rx <- factor(c(1, 2, 3, 1, 1, 3, 2, 3, 3), levels = 1:3,
             labels = c("g1", "g2", "g3"))
attributes(x)
typeof(x)
table(x)
```

**Date**

```r
today <- Sys.Date()
typeof(today)
attributes(today)
date <- as.Date("2019-10-31")
```

**missing values**

```r
NA
NaN
NULL
is.na()
is.nan()
is.null()
```

---
## Subsetting

.pull-left[

```r
*# atomic vector
x <- 1:5
length(x)
x[1]
x[-1]
x[1:3] <- 3:1
x[c(TRUE,TRUE,TRUE,FALSE,FALSE)]
names(x)
names(x) <- LETTERS[1:length(x)]
names(x)
x['E']
x[c('A', 'B', 'C')]
```

```
## [1] 5
## [1] 1
## [1] 2 3 4 5
## [1] 3 2 1
## NULL
## [1] "A" "B" "C" "D" "E"
## E 
## 5 
## A B C 
## 3 2 1
```
]

.pull-right[

```r
*# list
l <- list('one' = c('a', 'b', 'c'),
          'two' = 1:5,
          'three' = c(TRUE, FALSE)
          )
l[1]
l[[1]]
l[[1]][1]
l[['one']]
l$one
```

```
## $one
## [1] "a" "b" "c"
## 
## [1] "a" "b" "c"
## [1] "a"
## [1] "a" "b" "c"
## [1] "a" "b" "c"
```
]

---
background-image: url(data:image/png;base64,#https://pbs.twimg.com/media/CO2_qPVWsAAErbv?format=png&name=large)
background-size: contain

---
background-image: url(data:image/png;base64,#https://d33wubrfki0l68.cloudfront.net/2f3f752cae25018554d484464f117e600ff365a2/37627/diagrams/lists-subsetting.png)
background-size: 500px

```r
a <- list(a = 1:3, b = "a string", c = pi, d = list(-1, -5))
```

---
.pull-left[

```r
(m <- matrix(1:9, nrow = 3))
```

```
##      [,1] [,2] [,3]
## [1,]    1    4    7
## [2,]    2    5    8
## [3,]    3    6    9
```

```r
m[1:2, 2:3]
```

```
##      [,1] [,2]
## [1,]    4    7
## [2,]    5    8
```

```r
m[1, ]
```

```
## [1] 1 4 7
```

```r
m[1, , drop = FALSE]
```

```
##      [,1] [,2] [,3]
## [1,]    1    4    7
```
]

.pull-right[
```r
df <- data.frame(x = 1:4, y = 4:1, z = letters[1:4],
                 stringsAsFactors = FALSE)
df
df[c("x", "z")]
df[1:2]
df[1:2, ]
```
]

---
## Function

population standard deviation

`$$\sqrt{\frac{1}{n}\sum_{i=1}^{n}{(x-\bar{x})^2}}$$`

```r
x <- rnorm(10)
sqrt(mean((x - mean(x))^2))
```

```
## [1] 0.9054527
```

```r
# avoid repeat yourself
sd_pop <- function(x) {
  return(sqrt(mean((x - mean(x))^2)))
}
sd_pop(x)
```

```
## [1] 0.9054527
```

---
**How to view the source code?**

1. just type function name e.g. `sd`

1. if is S3 generic function, e.g. `mean`, use methods() to get the specific method like `mean.default`, or `getAnywhere()`

1. if S4, `getMethod()` may be helpful

1. base R source in [github](https://github.com/wch/r-source)

---
**Pipe %>%**

```r
* # low readability
leave_house(get_dressed(get_out_of_bed(wake_up(me))))

* # intermediate variables
me1 <- wake_up(me)
me2 <- get_out_of_bed(me1)
me3 <- get_dressed(me2)
leave_house(me3)

* # nice
me %>% 
  wake_up() %>% 
  get_out_of_bed() %>% 
  get_dressed() %>% 
  leave_house()

```

**Base R Cheatsheet**

RStudio: Help > Cheatsheets

[http://github.com/rstudio/cheatsheets/raw/master/base-r.pdf](http://github.com/rstudio/cheatsheets/raw/master/base-r.pdf)

---
class: inverse, center, middle

# 2. Data I/O
readr, readxl, DBI, vroom, data.table
---
**csv files**

- base R: `read.csv(), write.csv()`
- [readr](https://readr.tidyverse.org): `read_csv(), write_csv()`
- vroom: `vroom(), vroom_write()`
- data.table: `fread(), fwrite()`

**excel files**

`readxl::read_excel()`

**database**

`DBI::dbConnect()`

**Data Import Cheatsheet**

[https://rawgit.com/rstudio/cheatsheets/master/data-import.pdf](https://rawgit.com/rstudio/cheatsheets/master/data-import.pdf)

---
class: inverse, center, middle

# 3. Data Visualisation

ggplot2

---
## diamonds

```r
head(diamonds)
```

```
## # A tibble: 6 x 10
##   carat cut       color clarity depth table price     x     y     z
##   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
## 2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
## 3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31
## 4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
## 5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
## 6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48
```

---
## Base R

```r
hist(diamonds$price)
plot(density(diamonds$price))
boxplot(price~cut, diamonds)
```

---
## why [ggplot2](https://ggplot2.tidyverse.org)

> “R的基础绘图系统基本就是一个纸笔模型，即一块画布摆在面前，可以在这里画几个点，在那里画几条线，指哪儿画哪儿，但这不是让数据分析者说话的方式，数据分析者不会说这条线用#FE09BE颜色，那个点用三角形状，他们只会说，把图中的线用数据中的职业类型变量上色，或者图中点的形状对应性别变量，这是数据分析者的说话方式，而ggplot2正是以这种方式来表达的。”  ---谢益辉

---

```r
ggplot(diamonds) + aes(price) + 
  geom_histogram()
```

```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

```r
ggplot(diamonds) + aes(price) +
  stat_density(geom = 'line')

ggplot(diamonds) + aes(cut, price) +
  geom_boxplot()
```

---
**Template**

```r
ggplot(data = xxx, mapping = aes()) + 
  geom_xxx() or stat_xxx() + # one or more layers
  scale_xxx() + 
  coord_xxx() + 
  facet_xxx() + 
  theme_xxx()
```

```r
ggplot(diamonds) + aes(price) + 
  geom_histogram(aes(y= ..density..)) + 
  geom_density(color = 'blue')
```

---
**Geometric objects**

point, line, bar, box, text, ...

```r
ggplot(diamonds) + aes(carat, price) + 
  geom_point()

ggplot(diamonds) + aes(carat, price) + 
  geom_hex()
```

---
background-image: url(data:image/png;base64,#https://d33wubrfki0l68.cloudfront.net/70a3b18a1128c785d8676a48c005ee9b6a23cc00/7283c/images/visualization-stat-bar.png)
background-size: contain
background-position: 50% 90%

**Aesthetic mapping**

shape, size, color, linetype, fill, font size, ...

ggplot2 > vignettes

```r
ggplot(diamonds) + aes(cut) +
  geom_bar() + 
  theme_bw()
```

---

```r
ggplot(diamonds) + aes(cut, fill = cut) +
  geom_bar()

ggplot(diamonds) + aes(cut, fill = clarity) +
  geom_bar()

ggplot(diamonds) + aes(cut, fill = clarity) +
  geom_bar(position = 'dodge')
```

---
**facet**

```r
ggplot(diamonds) + aes(price) +
  geom_density() + 
  facet_grid(cut~.)
```

![](data:image/png;base64,#intro2r_files/figure-html/unnamed-chunk-14-1.png)

---
**plotly**

```r
p <- diamonds %>% 
  sample_n(500) %>% 
  ggplot() + aes(carat, price) +
  geom_point() + 
  facet_grid(.~cut)
plotly::ggplotly(p)
```

**ggplot2 Cheatsheet**

[https://github.com/rstudio/cheatsheets/blob/master/data-visualization-2.1.pdf](https://github.com/rstudio/cheatsheets/blob/master/data-visualization-2.1.pdf)

---
class: inverse, center, middle

# 4. Data Manipulation

tidyr, dplyr

---
## Tidy Data

> "Tidy datasets are all alike, but every messy dataset is messy in its onw way." --- Hadley Wickham

A dataset is tidy if:
  1. Each variable is in its own column.
  
  1. Each case is in its own row.
  
  1. Each value is in its own cell.

---
background-image: url(data:image/png;base64,#https://d33wubrfki0l68.cloudfront.net/6f1ddb544fc5c69a2478e444ab8112fb0eea23f8/91adc/images/tidy-1.png)
background-size: contain

---
background-image: url(data:image/png;base64,#https://rstudio-education.github.io/tidyverse-cookbook/images/tidyr-gather.png)

**gather**

```r
table4a %>% 
  gather(key = "year", value = "cases", 2:3)
  pivot_longer(2:3, names_to = "year", values_to = "cases")
```

---
background-image: url(data:image/png;base64,#https://rstudio-education.github.io/tidyverse-cookbook/images/tidyr-spread.png)
background-position: 50% 60%

**spread**

```r
table2 %>% 
  spread(key = type, value = count)
  pivot_wider(names_from = type, values_from = count)
```

---
background-image: url(data:image/png;base64,#https://rstudio-education.github.io/tidyverse-cookbook/images/tidyr-separate.png)

**separate**

```r
table3 %>% 
  separate(col = rate, into = c("cases", "pop"))
```

---
background-image: url(data:image/png;base64,#https://rstudio-education.github.io/tidyverse-cookbook/images/tidyr-unite.png)

**unite**

```r
table5 %>% 
  unite(col = "year", century, year, sep = "")
```

---
**select, filter**

```r
table1 %>% 
  select(country, year, population)
```

```
## # A tibble: 6 x 3
##   country      year population
##   <chr>       <int>      <int>
## 1 Afghanistan  1999   19987071
## 2 Afghanistan  2000   20595360
## 3 Brazil       1999  172006362
## 4 Brazil       2000  174504898
## 5 China        1999 1272915272
## 6 China        2000 1280428583
```

```r
table1 %>% 
  filter(year == 2000)
```

```
## # A tibble: 3 x 4
##   country      year  cases population
##   <chr>       <int>  <int>      <int>
## 1 Afghanistan  2000   2666   20595360
## 2 Brazil       2000  80488  174504898
## 3 China        2000 213766 1280428583
```

---
**mutate**

```r
table1 %>% 
  mutate(rate = cases/population, percent = rate * 100)
```

```
## # A tibble: 6 x 6
##   country      year  cases population      rate percent
##   <chr>       <int>  <int>      <int>     <dbl>   <dbl>
## 1 Afghanistan  1999    745   19987071 0.0000373 0.00373
## 2 Afghanistan  2000   2666   20595360 0.000129  0.0129 
## 3 Brazil       1999  37737  172006362 0.000219  0.0219 
## 4 Brazil       2000  80488  174504898 0.000461  0.0461 
## 5 China        1999 212258 1272915272 0.000167  0.0167 
## 6 China        2000 213766 1280428583 0.000167  0.0167
```

---
**summarise**

```r
table1 %>% 
  summarise(total_cases = sum(cases), max_rate = max(cases/population))
```

```
## # A tibble: 1 x 2
##   total_cases max_rate
##         <int>    <dbl>
## 1      547660 0.000461
```

```r
table1 %>% 
  group_by(country, year) %>% 
  summarise_at(vars(cases,population), list(min=min, mean=mean, max=max))
```

```
## # A tibble: 6 x 8
## # Groups:   country [3]
##   country     year cases_min population_min cases_mean population_mean cases_max
##   <chr>      <int>     <int>          <int>      <dbl>           <dbl>     <int>
## 1 Afghanist…  1999       745       19987071        745        19987071       745
## 2 Afghanist…  2000      2666       20595360       2666        20595360      2666
## 3 Brazil      1999     37737      172006362      37737       172006362     37737
## 4 Brazil      2000     80488      174504898      80488       174504898     80488
## 5 China       1999    212258     1272915272     212258      1272915272    212258
## 6 China       2000    213766     1280428583     213766      1280428583    213766
## # … with 1 more variable: population_max <int>
```

---
background-image: url(data:image/png;base64,#https://raw.githubusercontent.com/rstudio/cheatsheets/master/pngs/thumbnails/data-transformation-cheatsheet-thumbs.png)
background-size: contain

**Data Transformation Cheatsheet**

[https://github.com/rstudio/cheatsheets/blob/master/data-transformation.pdf](https://github.com/rstudio/cheatsheets/blob/master/data-transformation.pdf)

---
class: inverse, center, middle

# 5. Data Modeling

---
### About Max Kuhn

~~A package [caret](http://topepo.github.io/caret/index.html)~~

~~A book [Applied Predictive Modeling](http://appliedpredictivemodeling.com)~~

A book [Tidy Modeling with R](https://www.tmwr.org)

### About [Tidymodels](https://www.tidymodels.org)

- parsnip
 - recipes
 - rsample
 - workflows
 - yardstick
 - ...

---
class: inverse, center, middle

# 6. R Markdown

knitr, rmarkdown, bookdown, blogdown, pagedown

---
## Markdown?

A lightweight markup language.

Help > Markdown Quick Reference

---
background-image: url(data:image/png;base64,#https://d33wubrfki0l68.cloudfront.net/61d189fd9cdf955058415d3e1b28dd60e1bd7c9b/9791d/images/rmarkdownflow.png)

## Workflow

---
background-image: url(data:image/png;base64,#https://d33wubrfki0l68.cloudfront.net/aee91187a9c6811a802ddc524c3271302893a149/a7003/images/bandthree2.png)

## Rmd 生万物

---
## Beyond R

```r
names(knitr::knit_engines$get())
```

```
##  [1] "awk"       "bash"      "coffee"    "gawk"      "groovy"    "haskell"  
##  [7] "lein"      "mysql"     "node"      "octave"    "perl"      "psql"     
## [13] "Rscript"   "ruby"      "sas"       "scala"     "sed"       "sh"       
## [19] "stata"     "zsh"       "highlight" "Rcpp"      "tikz"      "dot"      
## [25] "c"         "cc"        "fortran"   "fortran95" "asy"       "cat"      
## [31] "asis"      "stan"      "block"     "block2"    "js"        "css"      
## [37] "sql"       "go"        "python"    "julia"     "sass"      "scss"     
## [43] "R"         "bslib"
```

---
## Demos

```r
install.packages('rticles')
install.packages('tinytex')
tinytex::install_tinytex()
```

---
## Suggested readings

1. [R for Data Science](https://r4ds.had.co.nz)

1. [ggplot2: Elegant Graphics for Data Analysis](https://ggplot2-book.org)

1. [现代统计图形](https://bookdown.org/xiangyun/msg/)

1. [R Markdown Cookbook](https://bookdown.org/yihui/rmarkdown-cookbook/)

1. [Advanced R](https://adv-r.hadley.nz)

---
class: center, middle

# _Thanks!_